| 摘要(英) |
The CD4 lymphocytes is an important indicator of diagnosis of AIDS, this paper mainly studied of AIDS, and investigates the association between the percentage of CD4 and the survival time, and explores the efficacy of HAART to AIDS patients. The patients repeated measurements the percentile of CD4 at different times as an interesting longitudinal data. The traditional approach have been employs the partial likelihood method to analyze the above data, but the partial likelihood method has to recognize the covariate history for each subject, and satisfy the requirements that the measurement error can not exist and the number of measurements should be enough for each subject. However, in the clinical trial, the individual differences, measurement error, and the other factors have usually result in bias. Therefore, we use the jointing model, fitting the survival time and longitudinal data simultaneously, to solve this problem. Here we focus on the generalized extended hazard model because Cox proportional hazards model and accelerated failure model are two special cases of the extended hazard model. Under a time-dependent covariates assumptions, there is no simple method to detect that the accelerated failure model is suitable for data fitting. To solve this problem, we use the regression parameters to do model selection through the Wald confidence interval, the percentile confidence interval and the bias-correction percentile confidence interval.
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| 參考文獻 |
[1] Brown, E. R., Ibrahim, J. G. and DeGruttola, V.(2005). A flexible B-spline model for multiple longitudinal biomarkers and survival.Biometrics 61, 64-73.
[2] Chen, Y. Q. and Jewell N. P. (2001). On a general class of semiparametric hazards regression models. Biometrka B 88, 687-702.
[3] Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Associtation 74, 829-836.
[4] Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological) 34, 187-220.
[5] Dafini, U. G. and Tsiatis, A. A. (1998). Evaluating surrogate markers of clinical outcome measured with error. Biometrics 54, 1445-1462.
[6] Dempster, A. P., Laird,N. M. and Rubin, D. B. (1977). Maximum Likelihood from Imcomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39, 1-38.
[7] Ding, J. and Wang, J. L. (2008). Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data. Biometrics 64, 546-556.
[8] Dubin, J. A., Müller, H. G. and Wang, J. L. (2001). Event history graphs for censored survival data. Statistics in Medicine 20, 2951-2964.
[9] Eforn, B. (1994). Missing Data, Imputation and the Bootstrap. Journal of American Statistical Association 89, 463-475.
[10] Ciampi, A. and Etezadi-Amoli, J. (1985). A general model for testing the proportional hazards and the accelerated failure time hypothesis in the analysis of censored survival data with covariate. Communications in Statistics 14, 651-667.
[11] Etezadi-Amoli J. and Campi A. (1987). Extended hazard regression for censored survival data with covariates: A spline approximation for the baseline hazard function. Biometrics B 43, 181-192.
[12] Henderson, R., Diggle, P. and Dobson, A. (2000). Joint modeling of longitudinal measurements and event time data. Biostatistics 1,465-480.
[13] Hsieh, F., Tseng, Y. K. and Wang, J. L. (2006). Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited.Biometrics 62,1037-1043.
[14] Lehmann, E. L. (1986). Testing Statistical Hypotheses. New York:Wiley.
[15] Louis,T. A. (1982). Finding the observed Fisher information when using the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 44, 226-233.
[16] Prentice, R.L. (1982). Covariate measurement errors and parameter estimation in failure time regression model. Biometrika 69, 331-342.
[17] Rosner, B. (2006). Fundamentals of Biostatistics (sixth edition). New York:Thomson Learning.
[18] Song, X., Davidian, M. and Tsiatis, A. A. (2002). A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data. Biometrics 58, 742-753.
[19] Taylor, J. M. G., Fahey, J. L., Detels, R., Giorgi, J.V. (1989). CD4 percentage, CD4 number and CD4: CD8 ratio in HIV infection: which to choose and how to use. Journal of Acquired Immune Deficiency Syndromes 2, 114-124.
[20] Tseng, Y. K., Hsieh, F. and Wang, J. L. (2005). Joint modeling of accelerated failure time and longitudinal data. Biometrika 92,587-603.
[21] Tsiatis, A. A. and Davidian, M. (2001). A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika 88, 447-458.
[22] Tsiatis, A. A., Degruttola, V. and Wulfsohn, M. S. (1995). Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Coutns in Patients with AIDS. Journal of
the American Statistical Association 90, 27-37.
[23] Wang, Y. and Taylor, J. M. G. (2001). Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome. Journal of the American Statistical Associiation 96, 895-905.
[24] Wulfsohn, M. S. and Tsiatis, A. A. (1997). A Joint Model for Survival and Longitudinal Data Measured with Error. Biometrics 53,330-339.
[25] Zeng, D. and Cai, J. (2005). Asymptotic Results for Maximum Likelihood Estimatiors in Joint Analysis of Repeated Measurements and Survival Time. The Annals of Statistics 33, 2132-2163.
[26] Zeng, D. and Lin, D. Y. (2007b). Efficient Estimation in the Accelerated Failure Time Model. Journal of the American Statistical Association 102,1387-1396.
[27] 行政院衛生署疾病管制局 出版 (2008). 愛滋病檢驗及治療指引.
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